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Optimizing Quantile-based Trading Strategies in Electricity Arbitrage

Ciaran O'Connor, Joseph Collins, Steven Prestwich, Andrea Visentin

Abstract

Efficiently integrating renewable resources into electricity markets is vital for addressing the challenges of matching real-time supply and demand while reducing the significant energy wastage resulting from curtailments. To address this challenge effectively, the incorporation of storage devices can enhance the reliability and efficiency of the grid, improving market liquidity and reducing price volatility. In short-term electricity markets, participants navigate numerous options, each presenting unique challenges and opportunities, underscoring the critical role of the trading strategy in maximizing profits. This study delves into the optimization of day-ahead and balancing market trading, leveraging quantile-based forecasts. Employing three trading approaches with practical constraints, our research enhances forecast assessment, increases trading frequency, and employs flexible timestamp orders. Our findings underscore the profit potential of simultaneous participation in both day-ahead and balancing markets, especially with larger battery storage systems; despite increased costs and narrower profit margins associated with higher-volume trading, the implementation of high-frequency strategies plays a significant role in maximizing profits and addressing market challenges. Finally, we modelled four commercial battery storage systems and evaluated their economic viability through a scenario analysis, with larger batteries showing a shorter return on investment.

Optimizing Quantile-based Trading Strategies in Electricity Arbitrage

Abstract

Efficiently integrating renewable resources into electricity markets is vital for addressing the challenges of matching real-time supply and demand while reducing the significant energy wastage resulting from curtailments. To address this challenge effectively, the incorporation of storage devices can enhance the reliability and efficiency of the grid, improving market liquidity and reducing price volatility. In short-term electricity markets, participants navigate numerous options, each presenting unique challenges and opportunities, underscoring the critical role of the trading strategy in maximizing profits. This study delves into the optimization of day-ahead and balancing market trading, leveraging quantile-based forecasts. Employing three trading approaches with practical constraints, our research enhances forecast assessment, increases trading frequency, and employs flexible timestamp orders. Our findings underscore the profit potential of simultaneous participation in both day-ahead and balancing markets, especially with larger battery storage systems; despite increased costs and narrower profit margins associated with higher-volume trading, the implementation of high-frequency strategies plays a significant role in maximizing profits and addressing market challenges. Finally, we modelled four commercial battery storage systems and evaluated their economic viability through a scenario analysis, with larger batteries showing a shorter return on investment.
Paper Structure (19 sections, 3 equations, 8 figures, 1 algorithm)

This paper contains 19 sections, 3 equations, 8 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivation and contributions
  3. Literature Review
  4. Probabilistic forecasting
  5. Energy Storage
  6. Trading
  7. Datasets
  8. Day-ahead Market
  9. Balancing Market
  10. Methodology
  11. Quantile Regression Models
  12. Trading Strategies
  13. TS1: Single trade quantile strategy
  14. TS2: Multi-Trade strategy
  15. TS3: High-Frequency Strategy
  16. ...and 4 more sections

Figures (8)

  • Figure 1: Annual percentages of curtailments, constraints, and dispatch down incidents, illustrating the impact of renewable energy challenges on wind output (Eirgrid).
  • Figure 2: MH RNN DNN Model
  • Figure 3: Average profit for TS1, TS2, & TS3 in each market. Error bars highlight the results for each quantile pair.
  • Figure 4: TS3 Profit for DAM, BM & DUAL quantile pairs. The error bars are relative to the different forecasting models.
  • Figure 5: TS3 Aggregate Pinball Score of Model vs Profit
  • ...and 3 more figures